Author
Weiwei Liu
Bio: Weiwei Liu is an academic researcher. The author has contributed to research in topic(s): Curvelet & Depth of field. The author has co-authored 2 publication(s).
Papers
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28 Aug 2020
TL;DR: Simulation experiments confirm that the new method of image denoising reduces the pseudo Gibbs phenomenon, retains the details and texture of the image better, and obtains better visual effects and higher PSNR values.
Abstract: To resolve the problems that the traditional image denoising methods are easy to lose details such as edges and textures, a new method of image denoising was proposed. It based on the Curvelet denoising algorithm, using polynomial interpolation threshold method, combining with Wrapping and Cycle spinning techniques to determine the adaptive threshold of each Curvelet coefficient for denoising the medical images. Simulation experiments confirm that the new method reduces the pseudo Gibbs phenomenon, retains the details and texture of the image better, and obtains better visual effects and higher PSNR values.
TL;DR: In this paper, a traffic image enhancement model based on illumination adjustment and depth of field difference is proposed to improve the clarity and color fidelity of traffic images under the complex environment of haze and uneven illumination and promote road traffic safety monitoring.
Abstract: In order to improve the clarity and color fidelity of traffic images under the complex environment of haze and uneven illumination and promote road traffic safety monitoring, a traffic image enhancement model based on illumination adjustment and depth of field difference is proposed. The algorithm is based on Retinex theory, uses dark channel principle to obtain image depth of the field, and uses spectral clustering algorithm to cluster image depth. After the subimages are divided, the local haze concentration is estimated according to the depth of field and the subimages are adaptively enhanced and fused. In addition, the illumination component is obtained by multiscale guided filtering to maintain the edge characteristics of the image, and the uneven illumination problem is solved by adjusting the curve function. The experimental results show that the proposed model can effectively enhance the uneven illumination and haze weather image in the traffic scene and the visual effect of the images is good. The generated image has rich details, improves the quality of traffic images, and can meet the needs of traffic practical application.